Contents 
import geopandas as gpd
from libpysal import graph
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import LogNorm
from matplotlib_scalebar.scalebar import ScaleBar
import pandas as pd
import contextily as cx

signatures = gpd.read_parquet("../../urbangrammar_samba/spatial_signatures/signatures/signatures_combined_levels_orig.pq")

# drop an outlier island
signatures = signatures.drop(96691)

signatures_urban = signatures[~signatures.signature_type.isin(["0_0", "4_0", "7_0", "9_6", '9_7', '9_8', '9_3'])].copy()

signatures_urban
kmeans10gb geometry level2 signature_type
10882 1 POLYGON Z ((143345.668 932691.413 0.000, 14334... 0.0 1_0
10883 1 POLYGON Z ((151573.910 30042.750 0.000, 151571... 0.0 1_0
10884 1 POLYGON Z ((151697.494 29760.709 0.000, 151650... 0.0 1_0
10885 1 POLYGON Z ((162909.562 26425.073 0.000, 162911... 0.0 1_0
10886 1 POLYGON Z ((164091.320 40526.696 0.000, 164093... 0.0 1_0
... ... ... ... ...
96686 9 POLYGON Z ((528530.170 181095.720 0.000, 52854... 5.0 9_5
96687 9 POLYGON Z ((528602.732 181135.450 0.000, 52857... 5.0 9_5
96688 9 POLYGON Z ((528643.907 181217.443 0.000, 52863... 5.0 9_5
96689 9 POLYGON Z ((528734.582 181316.822 0.000, 52873... 5.0 9_5
96690 9 POLYGON Z ((530336.433 181039.755 0.000, 53033... 5.0 9_5

68956 rows × 4 columns

%%time
contiguity = graph.Graph.build_contiguity(signatures_urban, rook=False)

CPU times: user 1min 15s, sys: 2.37 s, total: 1min 18s
Wall time: 1min 18s

%%time
signatures_urban["settlement"] = contiguity.component_labels

CPU times: user 38.7 ms, sys: 4.16 ms, total: 42.9 ms
Wall time: 44.5 ms

types = {
    "0_0": "Countryside agriculture",
    "1_0": "Accessible suburbia",
    "3_0": "Open sprawl",
    "4_0": "Wild countryside",
    "5_0": "Warehouse/Park land",
    "6_0": "Gridded residential quarters",
    "7_0": "Urban buffer",
    "8_0": "Disconnected suburbia",
    "2_0": "Dense residential neighbourhoods",
    "2_1": "Connected residential neighbourhoods",
    "2_2": "Dense urban neighbourhoods",
    "9_0": "Local urbanity",
    "9_1": "Concentrated urbanity",
    "9_2": "Regional urbanity",
    "9_4": "Metropolitan urbanity",
    "9_5": "Hyper concentrated urbanity",
}

signatures_urban["signature_type"] = signatures_urban["signature_type"].map(types)

signatures_urban.settlement.max()

9994

signatures_urban["area"] = signatures_urban.area

grouper = signatures_urban.groupby(["settlement", "signature_type"])
areas = grouper["area"].sum().unstack().fillna(0)

areas
signature_type Accessible suburbia Concentrated urbanity Connected residential neighbourhoods Dense residential neighbourhoods Dense urban neighbourhoods Disconnected suburbia Gridded residential quarters Hyper concentrated urbanity Local urbanity Metropolitan urbanity Open sprawl Regional urbanity Warehouse/Park land
settlement
0 98432.753668 0.0 658585.378043 82103.007472 16858.821454 3.867427e+04 194436.499973 0.0 0.0 0.000000 2.443473e+05 0.0 392625.177314
1 39159.397355 0.0 0.000000 0.000000 0.000000 4.303130e+03 0.000000 0.0 0.0 0.000000 0.000000e+00 0.0 0.000000
2 1714.392324 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 0.000000 7.653142e+02 0.0 0.000000
3 194126.224307 0.0 0.000000 0.000000 0.000000 1.116033e+06 245226.403996 0.0 0.0 0.000000 1.727041e+06 0.0 0.000000
4 98.243984 0.0 14951.756016 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 0.000000 0.000000e+00 0.0 0.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ...
9990 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 44838.650720 0.000000e+00 0.0 0.000000
9991 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 3069.922515 0.000000e+00 0.0 0.000000
9992 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 6332.353514 0.000000e+00 0.0 0.000000
9993 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 563.416847 0.000000e+00 0.0 0.000000
9994 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 6822.794200 0.000000e+00 0.0 0.000000

9995 rows × 13 columns

order = [
       'Warehouse/Park land', 'Open sprawl', 'Disconnected suburbia',
       'Accessible suburbia', 'Connected residential neighbourhoods',
       'Dense residential neighbourhoods', 'Gridded residential quarters',
       'Dense urban neighbourhoods', 'Local urbanity', 'Regional urbanity',
       'Metropolitan urbanity', 'Concentrated urbanity',
       'Hyper concentrated urbanity'
    ]
order.reverse()

areas_sorted = areas.sort_values(order, ascending=False)[order]

plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.iloc[:200].T, norm=LogNorm())

<Axes: xlabel='settlement', ylabel='signature_type'>
../_images/settlment_delin_14_1.png 
signatures_urban[signatures_urban.settlement == 1110].explore()
Make this Notebook Trusted to load map: File -> Trust Notebook
areas["total"] = areas.sum(axis=1)
areas.sort_values("total")
signature_type Accessible suburbia Concentrated urbanity Connected residential neighbourhoods Dense residential neighbourhoods Dense urban neighbourhoods Disconnected suburbia Gridded residential quarters Hyper concentrated urbanity Local urbanity Metropolitan urbanity Open sprawl Regional urbanity Warehouse/Park land total
settlement
4161 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.598476e-04 0.000000e+00 0.000000e+00 1.598476e-04
5362 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 5.270652e-03 0.000000e+00 0.000000e+00 5.270652e-03
7082 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.706335e-02 0.000000e+00 0.000000e+00 1.706335e-02
9457 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.720828e-02 1.720828e-02
1594 3.503358e-02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 3.503358e-02
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1024 8.444751e+07 0.000000e+00 1.337555e+07 3.137510e+07 2.479331e+07 1.552982e+07 2.332107e+06 0.000000e+00 6.522618e+06 1.001744e+05 2.786825e+08 1.905346e+06 1.668628e+07 4.757504e+08
1048 1.173756e+07 0.000000e+00 3.097936e+07 5.928168e+07 3.574174e+07 1.610781e+07 3.138634e+06 0.000000e+00 8.388703e+06 0.000000e+00 2.054696e+08 1.589767e+06 2.301838e+08 6.026186e+08
906 6.250311e+07 0.000000e+00 4.149977e+07 4.842811e+07 2.758493e+07 5.417000e+07 1.721331e+05 0.000000e+00 5.745378e+06 1.075760e+06 1.447693e+08 2.806010e+06 3.386016e+08 7.273561e+08
416 1.885914e+08 0.000000e+00 8.382661e+07 1.443043e+08 5.427237e+07 3.735568e+07 3.914361e+07 0.000000e+00 7.917305e+06 5.156702e+05 5.668313e+08 3.950295e+06 2.810229e+08 1.407731e+09
1916 2.132317e+08 7.884144e+06 1.364017e+08 2.235081e+08 2.201192e+08 8.389185e+07 6.699935e+06 1.306332e+06 1.155779e+08 1.321113e+07 2.989276e+08 3.960246e+07 3.818959e+08 1.742258e+09

9995 rows × 14 columns

areas["total"][areas["total"]<areas["total"].quantile(.75)].plot.hist(bins=150)

<Axes: ylabel='Frequency'>
../_images/settlment_delin_17_1.png 
areas["total"].quantile(.25)

524.8585925200732

areas["total"].median()

1266.1718319762128

plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.T, norm=LogNorm())

<Axes: xlabel='settlement', ylabel='signature_type'>
../_images/settlment_delin_20_1.png 
placenames = gpd.read_parquet("place.parquet")

%%time
joined = placenames.sjoin(signatures_urban[["geometry", "settlement"]])

CPU times: user 4.45 s, sys: 7.24 ms, total: 4.46 s
Wall time: 4.45 s

signatures_urban
kmeans10gb geometry level2 signature_type settlement area
10882 1 POLYGON Z ((143345.668 932691.413 0.000, 14334... 0.0 Accessible suburbia 0 9.843275e+04
10883 1 POLYGON Z ((151573.910 30042.750 0.000, 151571... 0.0 Accessible suburbia 1 2.597607e+03
10884 1 POLYGON Z ((151697.494 29760.709 0.000, 151650... 0.0 Accessible suburbia 1 3.656179e+04
10885 1 POLYGON Z ((162909.562 26425.073 0.000, 162911... 0.0 Accessible suburbia 2 1.714392e+03
10886 1 POLYGON Z ((164091.320 40526.696 0.000, 164093... 0.0 Accessible suburbia 3 1.941262e+05
... ... ... ... ... ... ...
96686 9 POLYGON Z ((528530.170 181095.720 0.000, 52854... 5.0 Hyper concentrated urbanity 1916 2.823163e+04
96687 9 POLYGON Z ((528602.732 181135.450 0.000, 52857... 5.0 Hyper concentrated urbanity 1916 1.797907e+03
96688 9 POLYGON Z ((528643.907 181217.443 0.000, 52863... 5.0 Hyper concentrated urbanity 1916 1.271663e+06
96689 9 POLYGON Z ((528734.582 181316.822 0.000, 52873... 5.0 Hyper concentrated urbanity 1916 1.872188e+03
96690 9 POLYGON Z ((530336.433 181039.755 0.000, 53033... 5.0 Hyper concentrated urbanity 1916 2.767724e+03

68956 rows × 6 columns

areas = signatures_urban.groupby("settlement")["area"].sum()

((areas / 1000000) < 1).sum()

9347

with_names = signatures_urban[signatures_urban.settlement.isin(joined.settlement)]

with_names.settlement.nunique()

1208

no_names = signatures_urban[~signatures_urban.settlement.isin(joined.settlement)]

no_names.settlement
kmeans10gb geometry level2 signature_type settlement area
10883 1 POLYGON Z ((151573.910 30042.750 0.000, 151571... 0.0 Accessible suburbia 1 2597.606832
10884 1 POLYGON Z ((151697.494 29760.709 0.000, 151650... 0.0 Accessible suburbia 1 36561.790523
10885 1 POLYGON Z ((162909.562 26425.073 0.000, 162911... 0.0 Accessible suburbia 2 1714.392324
10887 1 POLYGON Z ((166019.660 772318.979 0.000, 16601... 0.0 Accessible suburbia 4 98.243984
10889 1 POLYGON Z ((169531.730 648550.850 0.000, 16957... 0.0 Accessible suburbia 6 3074.587150
... ... ... ... ... ... ...
96585 9 POLYGON Z ((264877.672 43904.008 0.000, 264880... 4.0 Metropolitan urbanity 9990 44838.650720
96594 9 POLYGON Z ((330613.528 674133.509 0.000, 33061... 4.0 Metropolitan urbanity 9991 3069.922515
96647 9 POLYGON Z ((435236.214 575407.980 0.000, 43523... 4.0 Metropolitan urbanity 9992 6332.353514
96648 9 POLYGON Z ((435256.571 575404.037 0.000, 43525... 4.0 Metropolitan urbanity 9993 563.416847
96658 9 POLYGON Z ((525652.900 439269.310 0.000, 52565... 4.0 Metropolitan urbanity 9994 6822.794200

10821 rows × 6 columns

grouper = with_names.groupby(["settlement", "signature_type"])
areas = grouper["area"].sum().unstack().fillna(0)

areas_sorted = areas.sort_values(order, ascending=False)[order]

joined
local_type geometry name1 index_right settlement
2023027 Suburban Area POINT (418095.000 185406.000) Nythe 73480 1063
2049081 Suburban Area POINT (417576.000 184893.000) Park North 73480 1063
2023028 Suburban Area POINT (292127.000 90785.000) Marsh Barton 46382 291
2023029 Suburban Area POINT (295991.000 91247.000) Clyst Heath 23788 291
2040633 Suburban Area POINT (294268.000 91766.000) St Loye's 23788 291
... ... ... ... ... ...
2287377 Suburban Area POINT (430095.000 581544.000) Malvin's Close 30755 1048
2288413 Other Settlement POINT (395461.000 395566.000) The Railway Sidings 87974 416
2288557 Other Settlement POINT (206144.000 67004.000) Town End 21978 25
2288563 Other Settlement POINT (465287.000 103797.000) Vicarage Row 58015 1567
2288564 Other Settlement POINT (465332.000 103793.000) St Francis Court 58015 1567

10561 rows × 5 columns

plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.iloc[:200].T, norm=LogNorm())

<Axes: xlabel='settlement', ylabel='signature_type'>
../_images/settlment_delin_33_1.png 
plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.T, norm=LogNorm())

<Axes: xlabel='settlement', ylabel='signature_type'>
../_images/settlment_delin_34_1.png 
plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.T, norm=LogNorm(), cmap="Blues")

<Axes: xlabel='settlement', ylabel='signature_type'>
../_images/settlment_delin_35_1.png 
import urbangrammar_graphics as ugg
from matplotlib.colors import LinearSegmentedColormap

ugg.COLORS

[(0.19921875, 0.203125, 0.1953125),
 (0.23046875, 0.4296875, 0.55078125),
 (0.73828125, 0.35546875, 0.30859375),
 (0.5625, 0.640625, 0.4921875),
 (0.9375, 0.78125, 0.34375),
 (0.58203125, 0.3984375, 0.4296875)]

cmap = LinearSegmentedColormap.from_list("ugg", [ugg.COLORS[4], ugg.COLORS[3]])
cmap
ugg
ugg colormap
under
bad
over 
plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.T, norm=LogNorm(), cmap="cividis_r")

<Axes: xlabel='settlement', ylabel='signature_type'>
../_images/settlment_delin_39_1.png 
placenames
local_type geometry name1
2023027 Suburban Area POINT (418095.000 185406.000) Nythe
2023028 Suburban Area POINT (292127.000 90785.000) Marsh Barton
2023029 Suburban Area POINT (295991.000 91247.000) Clyst Heath
2023030 Suburban Area POINT (233883.000 84586.000) Kensey Valley Meadow
2023031 Suburban Area POINT (293538.000 93365.000) Polsloe
... ... ... ...
2289904 Other Settlement POINT (199430.000 907051.000) Garadheancal
2289905 Other Settlement POINT (636776.000 151377.000) Wingfield Place
2289906 Suburban Area POINT (480934.000 263680.000) Blackthorn
2289907 Hamlet POINT (140772.000 799096.000) Raonapoll
2289908 Suburban Area POINT (249437.000 628134.000) Hillhead Heights

43209 rows × 3 columns

with_names
kmeans10gb geometry level2 signature_type settlement area
10882 1 POLYGON Z ((143345.668 932691.413 0.000, 14334... 0.0 Accessible suburbia 0 9.843275e+04
10886 1 POLYGON Z ((164091.320 40526.696 0.000, 164093... 0.0 Accessible suburbia 3 1.941262e+05
10888 1 POLYGON Z ((167653.791 43194.946 0.000, 167654... 0.0 Accessible suburbia 5 9.879056e+03
10893 1 POLYGON Z ((170174.747 41387.203 0.000, 170172... 0.0 Accessible suburbia 8 1.610704e+05
10895 1 POLYGON Z ((175356.122 54184.967 0.000, 175365... 0.0 Accessible suburbia 10 6.876061e+03
... ... ... ... ... ... ...
96686 9 POLYGON Z ((528530.170 181095.720 0.000, 52854... 5.0 Hyper concentrated urbanity 1916 2.823163e+04
96687 9 POLYGON Z ((528602.732 181135.450 0.000, 52857... 5.0 Hyper concentrated urbanity 1916 1.797907e+03
96688 9 POLYGON Z ((528643.907 181217.443 0.000, 52863... 5.0 Hyper concentrated urbanity 1916 1.271663e+06
96689 9 POLYGON Z ((528734.582 181316.822 0.000, 52873... 5.0 Hyper concentrated urbanity 1916 1.872188e+03
96690 9 POLYGON Z ((530336.433 181039.755 0.000, 53033... 5.0 Hyper concentrated urbanity 1916 2.767724e+03

58135 rows × 6 columns

placenames.local_type.unique()

array(['Suburban Area', 'Hamlet', 'Village', 'Other Settlement', 'Town',
       'City'], dtype=object)

with_names.groupby("settlement")["signature_type"].nunique().value_counts() / with_names.settlement.nunique()

signature_type
2     0.284768
3     0.172185
1     0.147351
4     0.117550
5     0.068709
6     0.064570
7     0.050497
8     0.040563
9     0.033113
10    0.011589
11    0.008278
13    0.000828
Name: count, dtype: float64

%%time
settlement_boundaries = with_names[["geometry", "settlement"]].dissolve("settlement")

CPU times: user 2min 5s, sys: 0 ns, total: 2min 5s
Wall time: 2min 5s

names = []
for poly in settlement_boundaries.geometry:
    names_in = placenames.iloc[placenames.sindex.query(poly, predicate="contains")]
    if names_in.shape[0] == 1:
        names.append(names_in.name1.iloc[0])
    elif (names_in.local_type == "City").any():
        names.append(names_in[names_in.local_type == "City"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Town").any():
        names.append(names_in[names_in.local_type == "Town"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Suburban Area").any():
        names.append(names_in[names_in.local_type == "Suburban Area"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Village").any():
        names.append(names_in[names_in.local_type == "Village"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Hamlet").any():
        names.append(names_in[names_in.local_type == "Hamlet"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Other Settlement").any():
        names.append(names_in[names_in.local_type == "Other Settlement"].name1.str.cat(sep=' - '))
    else:
        names.append(None)

settlement_boundaries["name"] = names

settlement_boundaries.to_parquet("settlement_boundaries.parquet")

settlement_boundaries["area"] = settlement_boundaries.area / 1000000

settlement_boundaries.set_geometry(settlement_boundaries.simplify(100)).explore(tiles="CartoDB positron", popup=True)
Make this Notebook Trusted to load map: File -> Trust Notebook
fua = gpd.read_parquet("../../urbangrammar_samba/spatial_signatures/esda/fua.pq")
ttwa = gpd.read_parquet("../../urbangrammar_samba/spatial_signatures/esda/ttwa.pq")

m = settlement_boundaries.set_geometry(settlement_boundaries.simplify(100)).explore(tiles="CartoDB positron")
fua.explore(m=m, color='red')
Make this Notebook Trusted to load map: File -> Trust Notebook
bds_plot = settlement_boundaries.set_geometry(settlement_boundaries.simplify(100)).to_crs(3857)
fua_plot = fua.to_crs(3857)

ttwa.explore(tiles="cartodb voyager")
Make this Notebook Trusted to load map: File -> Trust Notebook
tiles = cx.providers.CartoDB.Positron
tiles_labels = cx.providers.CartoDB.PositronOnlyLabels

from matplotlib.patches import Patch

ax = bds_plot.plot(color=ugg.COLORS[1], figsize=(10, 10), zorder=10, alpha=.8)
# bds_plot.boundary.plot(ax=ax, color=ugg.COLORS[0], linewidth=.75)
fua_plot.plot(ax=ax, alpha=.7, color=ugg.COLORS[4])
# fua_plot.boundary.plot(ax=ax, alpha=1, color=ugg.COLORS[4], linewidth=1)
fua_plot.boundary.plot(ax=ax, alpha=1, color=ugg.COLORS[0], linewidth=.8)
ax.set_ylim(6400000, 7250000)
ax.set_xlim(-650000, 220000)
ax.set_axis_off()
cx.add_basemap(ax=ax, source=tiles.build_url(scale_factor="@2x"), attribution=tiles.attribution)

legend_elements = [
    Patch(facecolor=ugg.COLORS[1], label='Settlement area'),
    Patch(facecolor=ugg.COLORS[4], alpha=.7, edgecolor=ugg.COLORS[0], linewidth=.8, label='Functional urban area'),
]
ax.legend(handles=legend_elements, loc='upper right', frameon=False)

scalebar = ScaleBar(dx=1,
                    color=ugg.COLORS[0],
                    location='lower right',
                    height_fraction=0.002,
                    pad=.5,
                    frameon=False,
                    )
ax.add_artist(scalebar)

plt.savefig("figs/overlay_set_fua.png", dpi=144, bbox_inches="tight")
../_images/settlment_delin_56_0.png 
ax = bds_plot.plot(color=ugg.COLORS[1], figsize=(10, 10), zorder=10, alpha=.8)
bds_plot.boundary.plot(ax=ax, color=ugg.COLORS[0], linewidth=.75)
# fua_plot.plot(ax=ax, alpha=.7, color=ugg.COLORS[4])
ax.set_ylim(7470000, 7600000)
ax.set_xlim(-550000, -300000)
ax.set_axis_off()
cx.add_basemap(ax=ax, source=tiles.build_url(scale_factor="@2x"), attribution=tiles.attribution)

legend_elements = [
    Patch(facecolor=ugg.COLORS[1], label='Settlement area'),
]
ax.legend(handles=legend_elements, loc='upper left', frameon=True)

scalebar = ScaleBar(dx=1,
                    color=ugg.COLORS[0],
                    location='lower right',
                    height_fraction=0.002,
                    pad=.5,
                    frameon=False,
                    )
ax.add_artist(scalebar)

plt.savefig("figs/settlements_scotland.png", dpi=144, bbox_inches="tight")
../_images/settlment_delin_57_0.png 
ugg.CMAP
from_list
from_list colormap
under
bad
over 
fua.area.max() / 1000000

6605.0

settlement_boundaries
geometry name area
settlement
0 POLYGON Z ((142667.472 932126.244 0.000, 14264... Stornoway 1.726063
3 POLYGON Z ((163950.904 40031.623 0.000, 163950... Camborne 3.282427
5 POLYGON Z ((167036.075 42671.447 0.000, 167034... Illogan 0.593929
8 POLYGON Z ((169788.398 41575.672 0.000, 169777... Redruth 2.543934
10 POLYGON Z ((175581.974 54311.766 0.000, 175582... Perranporth 0.383067
... ... ... ...
9497 POLYGON Z ((501071.199 203479.014 0.000, 50107... Bovingdon 0.298797
9526 POLYGON Z ((504988.264 302841.508 0.000, 50499... Wittering 0.444567
9592 POLYGON Z ((515161.410 416170.202 0.000, 51516... None 0.015553
9777 POLYGON Z ((560071.598 165353.771 0.000, 56007... New Ash Green 0.644549
9822 POLYGON Z ((584270.281 332038.541 0.000, 58427... Wicken Green Village 0.066374

1208 rows × 3 columns

(settlement_boundaries['area'] < 1).sum()

584

settlement_boundaries.loc[[5271]].explore()
Make this Notebook Trusted to load map: File -> Trust Notebook
areas_sorted["name"] = settlement_boundaries["name"]

plot_data = areas_sorted.set_index("name", drop=True)

plot_data.index.name = "Settlement"
plot_data.columns.name = "Signature type"

plt.subplots(figsize=(16, 4))
ax = sns.heatmap(data=plot_data.T, norm=LogNorm(), cmap="cividis_r")
plt.tick_params(
    axis='x',     
    which='both', 
    bottom=False,     
    top=False,         
    labelbottom=False)
plt.savefig("hierarchy.pdf")
../_images/settlment_delin_66_0.png 
plot_data.index[12:20]

Index(['Plymouth', 'Sunderland - Newcastle upon Tyne', 'Aberdeen',
       'Nottingham', 'Norwich', 'Scarborough',
       'Skelton - Saltburn-by-the-Sea - Brotton - Marske-by-the-Sea - Redcar - Yarm - Thornaby-on-Tees - Stockton-on-Tees - Billingham - Middlesbrough - Hartlepool',
       'Abertawe'],
      dtype='object', name='Settlement')

plot_data.index[1040:1045]

Index(['Coundon', 'New Ash Green', 'Hamble-le-Rice', None, 'Long Buckby'], dtype='object', name='Settlement')

settlement_sig_area = with_names.groupby(["settlement", "signature_type"])["area"].sum()

settlement_sig_area.xs("Accessible suburbia", level=1)

settlement
0       9.843275e+04
3       1.941262e+05
5       9.879056e+03
8       1.610704e+05
10      6.876061e+03
            ...     
2759    2.782918e+05
2760    7.113730e+05
2761    3.394846e+06
2762    7.550956e+06
2765    2.403334e+04
Name: area, Length: 857, dtype: float64

settlement_boundaries.assign(sig_area=settlement_sig_area.xs("Gridded residential quarters", level=1) / settlement_boundaries.area).plot("sig_area", figsize=(20, 20), scheme="quantiles", k=10s).set_axis_off()
../_images/settlment_delin_71_0.png 
settlement_boundaries.assign(sig_area=settlement_sig_area.xs("Dense urban neighbourhoods", level=1) / settlement_boundaries.area).plot("sig_area", figsize=(20, 20), scheme="quantiles", k=10).set_axis_off()
../_images/settlment_delin_72_0.png 
with_names.groupby(["signature_type"])["area"].sum().sort_values() / 1000000

signature_type
Hyper concentrated urbanity                1.306332
Concentrated urbanity                      7.884144
Metropolitan urbanity                     16.523881
Regional urbanity                         76.432434
Local urbanity                           231.114682
Gridded residential quarters             260.548485
Connected residential neighbourhoods     564.556224
Dense urban neighbourhoods               570.643221
Disconnected suburbia                    698.800932
Dense residential neighbourhoods         956.964941
Accessible suburbia                     2194.269106
Warehouse/Park land                     2441.457800
Open sprawl                             4968.897217
Name: area, dtype: float64